Evidence map›Paper›PMID 38817397›Full record

ReviewEvolutionary applications2024

Antimicrobial resistance in the wild: Insights from epigenetics.

Mariana Villalba de la Peña, Ilkka Kronholm

Abstract readReview
In one paragraph

Review in Evolutionary applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mariana Villalba de la PeñaDepartment of Biological and Environmental Science University of Jyväskylä Jyväskylä Finland.ORCID https://orcid.org/0000-0002-3912-8437
Ilkka KronholmDepartment of Biological and Environmental Science University of Jyväskylä Jyväskylä Finland.ORCID https://orcid.org/0000-0002-4126-0250

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spreading of bacterial and fungal strains that are resistant to antimicrobials poses a serious threat to the well-being of humans, animals, and plants. Antimicrobial resistance has been mainly investigated in clinical settings. However, throughout their evolutionary history microorganisms in the wild have encountered antimicrobial substances, forcing them to evolve strategies to combat antimicrobial action. It is well known that many of these strategies are based on genetic mechanisms, but these do not fully explain important aspects of the antimicrobial response such as the rapid development of resistance, reversible phenotypes, and hetero-resistance. Consequently, attention has turned toward epigenetic pathways that may offer additional insights into antimicrobial mechanisms. The aim of this review is to explore the epigenetic mechanisms that confer antimicrobial resistance, focusing on those that might be relevant for resistance in the wild. First, we examine the presence of antimicrobials in natural settings. Then we describe the documented epigenetic mechanisms in bacteria and fungi associated with antimicrobial resistance and discuss innovative epigenetic editing techniques to establish causality in this context. Finally, we discuss the relevance of these epigenetic mechanisms on the evolutionary dynamics of antimicrobial resistance in the wild, emphasizing the critical role of priming in the adaptation process. We underscore the necessity of incorporating non-genetic mechanisms into our understanding of antimicrobial resistance evolution. These mechanisms offer invaluable insights into the dynamics of antimicrobial adaptation within natural ecosystems.

Indexed as

adaptationantimicrobial resistanceepigeneticsmicrobesnatural environment

Identifiers

PMID38817397
PMCPMC11134192

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.